Tag: product tours

Director of Product, Growth & AI at Amplitude: My Playbook for Viral Growth and Engagement

I see the Director of Product, Growth & AI at Amplitude as a mandate to operationalize "viral and core growth strategies, user acquisition, and product engagement" with precision. From my vantage point, that means building a rigorous, metrics-first operating system grounded in Amplitude analytics and product-led growth principles, then layering in an AI Strategy that personalizes experiences without sacrificing control or safety.

I start by defining a clear North Star Metric and mapping a driver tree to expose causal levers across acquisition, activation, engagement, retention, and monetization. With behavioral analytics and cohort analysis, I quantify which user behaviors correlate with long-term value. I operationalize rapid experimentation through A/B testing with sensible minimum detectable effect (MDE) thresholds, guardrail metrics, and sequential testing to ensure we move fast while preserving measurement integrity.

For "viral and core growth strategies," I lean on durable growth loops more than one-off hacks. Viral loops might include collaboration invites, user-generated content, and shareable artifacts that make the product more valuable as it spreads. Core growth centers on frictionless activation: guided onboarding, in-app guides, product tours, progressive disclosure, and judicious tooltip design that connects users to the ‘aha’ moment quickly. Session replay and funnel instrumentation help isolate friction and systematically remove it.

On user acquisition, I connect performance channels and go-to-market strategy tightly to in-product activation. Rather than optimizing for clicks, I optimize for post-signup behaviors that predict retention. This includes improving landing page-message-product congruence, refining qualification (so top-of-funnel aligns with downstream value), and orchestrating lifecycle messaging that nudges users toward key activation milestones.

To deepen product engagement, I focus on leading indicators of retention and feature adoption. I segment by jobs-to-be-done and intent, then personalize in-app prompts to surface the right capability at the right moment. Retention analysis, pathing, and funnel breakouts inform which nudges to deploy and where—whether that’s smarter checklists, contextual education, or lightweight in-product interventions that turn sporadic usage into reliable habits.

AI raises the ceiling on what’s possible here. With a thoughtful AI Strategy, I use gen ai to personalize onboarding flows, recommend next-best actions based on behavioral signals, and summarize complex activity patterns into actionable insights for the team. I maintain strict measurement: every AI intervention ships behind feature flags, is evaluated through controlled experiments, and adheres to privacy-by-design principles. The outcome is a system that learns continuously while staying aligned to business and user outcomes.

Execution is where strategy becomes real. I rely on empowered product trios, continuous discovery with customers, and outcome-focused roadmaps that tie directly to the driver tree. This keeps the organization moving in sync: engineering prioritizes the highest-signal experiments, design accelerates comprehension and task success, and product ensures each release strengthens the core loop rather than adding ornamental features.

Ultimately, the blueprint is simple and disciplined: anchor on "viral and core growth strategies, user acquisition, and product engagement," quantify what matters with behavioral analytics, and iterate through well-instrumented experiments. Combine that with targeted AI augmentation, and you create a compounding growth engine that is both measurable and resilient.

Inspired by this post on Amplitude – Perspectives.

June 5, 2026
Jumpstart Your Analytics Mastery: The Amplitude Quickstart Series for Faster, Smarter Insights

I’m excited to share a resource I recommend to every product and growth team I mentor: the Amplitude Quickstart Series. It’s a concise, approachable way to build confidence in “Amplitude analytics” and turn behavioral data into decisions that actually move the needle.

Discover user-friendly videos that walk you through Amplitude’s most essential products and features.

In my role leading product teams, I’ve seen how a clear, opinionated path through a “unified analytics platform” reduces time-to-insight from weeks to days. The Quickstart format makes it easy for product managers, analysts, and marketers to align on a common language for “behavioral analytics,” so we spend less time debating definitions and more time shipping value.

What I appreciate most is how quickly these lessons translate into outcomes: crisper instrumentation practices, cleaner dashboards, and sharper questions that drive “product-led growth.” That foundation accelerates “user activation,” improves “retention analysis,” and ultimately leads to better prioritization and stronger roadmap bets.

My recommended workflow: watch the entire series once to map the mental model, then revisit each segment as you operationalize it. Pair the guidance with a lightweight tracking plan, establish clear event naming conventions, and document your first key use cases (e.g., activation funnel, onboarding drop-off, core feature adoption). This cadence helps teams institutionalize good habits without over-engineering.

For cross-functional leaders, the series is also a powerful alignment tool. Ask product, data, design, and customer success to watch the same modules, then run a joint working session to define success metrics and accountability. When everyone sees the same “north-star” dashboards, decision-making speeds up and the quality of trade-offs improves.

As your practice matures, amplify the impact by pairing insights with action: connect findings to experiments, “feature flags,” and iterative product tours; complement quantitative patterns with “session replay” for richer context. This closed-loop approach helps you move from reporting to repeatable, insight-to-execution cycles.

If you’re new to Amplitude or scaling a growing practice, this Quickstart Series is the shortest path I know from curiosity to competence. Watch it, implement one improvement per week, and share progress broadly—momentum compounds.

Inspired by this post on Amplitude – Best Practices.

May 8, 2026
Build vs. Buy for Churn Prediction: My Proven Playbook for Faster Retention and ROI

Churn is the silent tax on growth, and I treat churn prediction as a core product capability—not a side project. Over the years, I’ve led teams through multiple implementations across different data maturities and go-to-market motions, and the same question keeps returning at kickoff: what’s the smartest path to impact now and defensibility later?

“Should you build or buy your churn prediction model?” The right answer depends on time-to-value, data readiness, available talent, and whether churn prediction is a true differentiator for your product strategy or simply a must-have capability to power customer success and product-led growth.

When speed and coverage matter most, I start by evaluating category platforms that pair behavioral analytics with activation. As one example, vendors emphasize immediate business outcomes such as integrations, in-app guides, and workflow triggers that help you act on risk signals fast—without waiting months for model training or data engineering.

Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.

Buying makes sense when you need rapid time-to-value, opinionated best practices, and a unified analytics platform to operationalize insights through product tours, in-app guides, and CRM integration. In these cases, I’m optimizing for coverage, consistent signal quality, and ease of activation for customer success—so the team can focus on interventions, not infrastructure.

Building is compelling when churn prediction is a source of competitive differentiation or you have proprietary signals others can’t access. If your product generates unique behavioral data, requires custom anomaly detection or explainability constraints, or must blend usage telemetry with domain-specific risk scoring, a tailored model can raise precision and unlock novel retention levers.

My hybrid approach has become a reliable playbook: buy first to establish a strong baseline and close the activation loop, then selectively build where proprietary data and context yield outsized gains. I use retention analysis to identify high-signal behaviors, then iterate with A/B testing and a clear minimum detectable effect (MDE) to validate uplift before committing engineering capacity.

Total cost of ownership is non-negotiable. I account for more than license or training costs: ongoing data engineering, feature pipeline maintenance, model monitoring for drift, and AI risk management all add up. Strong data governance, privacy-by-design, and regulatory compliance must be baked in—whether I build, buy, or blend both.

Activation determines real ROI. Predictions that don’t flow into customer success workflows, lifecycle messaging, or in-product nudges rarely move Net Recurring Revenue (NRR). I prioritize tight integrations that enable targeted experiments—journey mapping, contextual tooltips, and timely outreach—to reduce friction and increase user engagement at the moments that matter.

My quick decision test: buy if time-to-value and adoption are the immediate goals; build if proprietary signals and explainability are core strategic assets; blend if you want fast wins now with room to differentiate later. Answering the build vs. buy question through this lens consistently improves retention, accelerates product-led growth, and keeps teams focused on the customer experience rather than plumbing.

Inspired by this post on Pendo – Perspectives.

April 16, 2026
Inside Banani: How a Canvas-First AI Designer Elevates UX and Accelerates Product Teams

I believe the future of product design isn’t about replacing designers—it’s about giving every team access to one. That’s why Banani grabbed my attention. It’s an AI product designer that doesn’t just generate code—it generates design. For solo founders, stretched design teams, and early-stage startups, that shift matters: it raises the design floor without lowering the creative ceiling.

I spent time with Vlad Solomakha (CEO & Co-founder), Vova Kovalchuk (CTO & Co-founder), and Vlad Ostapovats (Founding Growth) to unpack how they took Banani from a Figma plugin proof-of-concept to a canvas-first AI design tool generating hundreds of thousands of designs per week. Vlad brings a decade of design experience and a precise north star: AI should produce beautiful, tasteful design rather than average, undifferentiated UI.

The architectural choices stood out. They engineered their agent to handle parallel screen edits, manage per-screen context across canvases with hundreds of frames, and make surgical edits without regenerating entire screens. This is the kind of agentic AI work that product leaders have been waiting for: concrete advances in context window management, tool orchestration, and prompt engineering that translate into higher throughput without sacrificing quality.

Equally important is how they addressed the "gulf of specification"—the mismatch between how designers think visually and how agents understand text. Banani’s canvas-first approach acknowledges that design is spatial, hierarchical, and iterative. Rather than forcing a chat-first UX, they center the canvas and let the agent do production work while keeping the designer firmly in control. In practice, this narrows intent ambiguity, speeds up iteration, and preserves taste.

The team made another pivotal bet: Why Banani doesn’t compile running applications — just HTML/CSS mockups — and how that shapes everything. By decoupling the design artifact from runnable code, they optimize for velocity, taste, and exploration. In my experience, this separation is the right product strategy for early discovery and gen ai for product prototyping—move fast on aesthetics and flows, then converge on implementation once you’ve validated the direction.

I also appreciated their pragmatic evaluation approach. Instead of traditional evals, they spin up 10 screens from one prompt to compare models. It’s hands-on, outcome-based, and aligned with eval-driven development in real product environments. They’re relentlessly discerning about when to work around model limitations versus when to wait for the models to improve—an essential discipline when building at the edge of what’s possible.

Under the hood, context engineering and specialized agent tools do the heavy lifting. Per-screen history with shared project context enables precise, reversible changes across large canvases. The result: fewer destructive regenerations, more reliable design intent preservation, and a workflow that feels like collaborating with a strong mid-level designer who’s exceptionally fast and consistent.

If you want a quick tour, I recommend jumping to a few highlights: 20:13 Product Tour Canvas First AI, 33:40 Gulf of Specification, 42:54 Agent Architecture Under Hood, 48:48 State History Context Tricks, and 56:04 Navigating Busy Canvases. Each segment reveals a different layer of the system design and product thinking behind Banani’s canvas-first UX.

For product leaders, this is a compelling blueprint for raising the design floor while protecting the last mile of craft. It aligns with empowered product teams, continuous discovery, and LLMs for product managers who need leverage without losing judgment. If you’re exploring agentic AI in design, this is a thoughtful, execution-focused model worth studying and trialing on your next product tour or redesign.

Resources worth exploring: Banani and TL Draw. To hear the full conversation, you can listen on Spotify or Apple Podcasts. Then, pressure-test the approach inside your own product development lifecycle and see how a canvas-first AI designer reshapes your team’s velocity and quality bar.

Inspired by this post on Product Talk.

April 2, 2026
Docs-as-Code Leadership at Scale: How Jeff Scattini Elevates End-to-End Product Documentation

Great products aren’t just shipped; they’re understood. In my product management practice, the difference between a good release and a great one often comes down to disciplined documentation that moves at the speed of delivery. That’s why the docs-as-code approach has become a cornerstone of how I build, lead, and measure product experiences across teams.

As I reflect on leaders who set a high bar in this craft, one description stands out: "With years of experience as Senior Documentation Manager, Jeff leads teams and oversees the end-to-end creation of documentation using docs-as-code methodology." That concise statement captures a model I deeply respect—one that treats documentation as a first-class citizen in the product lifecycle.

In practice, docs-as-code integrates documentation into CI/CD pipelines, version control, and peer review workflows—exactly how we ship software. This elevates quality, enforces consistency, and accelerates responsiveness to change, all while enabling rigorous content audit and UX writing standards. When documentation evolves with code, it becomes discoverable, testable, and measurable—key traits for scalable product management leadership.

The downstream impact is tangible. Users ramp faster through onboarding, in-app guides, and product tours because the narrative aligns with the product’s true state at any given commit. Support tickets drop, developers work with greater clarity, and PMs gain the feedback loops needed for continuous discovery. In a product-led growth motion, this clarity compounds—reducing time-to-value and enabling teams to ship confidently.

Equally important is the leadership pattern behind the methodology: aligning product, engineering, and customer-facing teams around shared truths. I’ve seen empowered product teams operate at their best when documentation is embedded in planning, sprint reviews, and release gates. This creates a single source of truth that scales knowledge, preserves intent, and shortens the path from decision to delivery.

For me, the standard expressed above isn’t just a role description—it’s a blueprint for operational excellence. When we manage documentation with the same rigor as code, we build trust at every touchpoint and create the conditions for sustained product velocity. That’s the level of clarity and execution I strive to foster across every product line.

Inspired by this post on Amplitude – Perspectives.

March 20, 2026
Unlock High-Impact Mobile Engagement: Amplitude Guides & Surveys for iOS, Android, React Native

Mobile engagement is most effective when it’s timely, contextual, and grounded in real user behavior. In my experience leading product teams, the fastest path to activation and retention comes from meeting users in the moment with relevant in-app guides and lightweight surveys that reduce friction and illuminate intent.

Deploy behavioral-driven mobile engagement with Amplitude Guides and Surveys for iOS, Android, and React Native platforms.

What excites me about this approach is how naturally it supports product-led growth. In-app guides and product tours streamline onboarding, while targeted micro-surveys surface the “why” behind user actions. The result: clearer journey mapping, fewer blind spots in the funnel, and a smoother path to user activation—all without adding engineering heavy-lift for each iteration.

To optimize continuously, I pair behavioral analytics with A/B testing and retention analysis. This lets my team validate hypotheses quickly, localize friction by segment or stage, and tune messaging for different cohorts. With Amplitude analytics at the core, we can connect engagement nudges to downstream outcomes, not just clicks—so we’re improving time-to-value, not just surface metrics.

My recommended starting point is simple: define a single activation moment, instrument the critical behaviors around it, and launch a focused guide plus one survey to test the narrative. Use journey mapping to identify the key decision points, then iterate weekly based on observed behavior, not opinions. This cadence keeps learning velocity high and ensures every change moves us closer to clear outcomes.

From a leadership perspective, I coach product trios to own an activation or retention KPI, run small controlled experiments, and document learning with crisp before/after evidence. Cross-platform support across iOS, Android, and React Native means we can scale wins quickly, standardize patterns, and create a repeatable playbook for new features and markets—all while keeping the user experience coherent and respectful.

Inspired by this post on Amplitude – Best Practices.

March 11, 2026
Mastering NRR: How Great Customer Success Teams Drive Expansion, Crush Churn, and Scale PLG

Net Recurring Revenue (NRR) is the cleanest truth-teller in my operating system. When I review NRR, I’m not just looking at whether we renewed accounts—I’m assessing whether our product and customer success motions are compounding revenue from our existing customers. Put simply: good CS teams protect revenue; great CS teams grow it through adoption, expansion, and durable retention.

Here’s how I frame NRR with my teams: it reflects revenue from our current customers after expansion, downgrades, and churn. If it’s at or above 100%, the installed base is self-sustaining; if it’s materially above 100%, the base is funding growth without net-new sales. That’s the holy grail for product-led growth and the benchmark I use to separate good from great.

At HighLevel, I’ve learned that you can’t “wish” your way to high NRR. You operationalize it. We align incentives, dashboards, and rituals so everyone—from PMs to CSMs to Solutions Engineering—owns the same outcome. Our “QBRs vs OKRs” discussions anchor on NRR drivers: activation rates, time-to-value, feature adoption depth, and expansion readiness. Those leading indicators tell me where we’ll land on lagging revenue results.

The best Customer Success teams operate like product teams. They use behavioral analytics and retention analysis to segment customers by use case and maturity, then design journey mapping to move each segment from first value to habitual value. They proactively reduce risk while creating clear expansion paths—new seats, premium features, or higher-tier plans—based on real product usage, not guesswork.

Onboarding is where great NRR trajectories begin. I focus on compressing time-to-first-value and time-to-second-value because those moments create the habit loops that underpin renewal and expansion. In practice, that means targeted in-app guides, contextual product tours, and nudges that drive user activation across the “sticky” features that correlate most with long-term retention.

To make this scalable, we blend human and product-led touchpoints. CSMs run outcome-based playbooks, while the product experience handles education and reinforcement at scale. When usage signals an expansion opportunity—say, a team consistently bumps into plan limits—we generate a product-qualified expansion lead and equip the CSM with the exact value storyline and proof points to close it.

Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.

I’ve seen this playbook move the needle. After instrumenting our key workflows and deploying targeted in-app guidance, we watched adoption of our highest-retaining features climb, risk flags surface earlier, and expansion conversations become far more data-driven. We didn’t chase shiny objects; we built a reliable pipeline of retained and expanded revenue directly from product usage.

If you’re aiming to level up NRR, start with a crisp blueprint: define the critical events that predict renewal and expansion; set activation milestones per segment; deploy in-app guides and product tours to remove friction; give CSMs a single-pane view of risk and readiness; and review NRR weekly with the same seriousness you apply to new ARR. Consistency beats intensity here.

Finally, keep the narrative simple. Your leadership story isn’t “we shipped features,” it’s “we created customer outcomes.” Tie every CS and product initiative back to NRR drivers—and make the wins visible. When teams see the direct line from great onboarding and adoption to measurable expansion, they naturally operate like a unified, product-led growth engine.

NRR rewards rigor. Treat it as the top-line health metric for your installed base, make the software do more of the teaching, and empower CS to coach to outcomes. Do that well, and you won’t just separate the good from the great—you’ll build a compounding machine.

Inspired by this post on Pendo – Best Practices.

March 11, 2026
Pendomonium 101: Insider Tips, Proven Strategies, and Why This Product Festival Is Can’t‑Miss

Every year, I circle Pendomonium on my calendar because it reliably delivers the perfect blend of strategy, execution, and community. It’s where product leaders, builders, and operators compare notes on what actually moves activation, adoption, and retention—and where I pressure-test my roadmap and go-to-market assumptions against real-world data and peer experience.

Pendomonium is a product festival by Pendo in downtown Raleigh. Get answers to all your questions about the best product festival of the year.

From a product management leadership lens, the value is clear: Pendomonium is a concentrated learning loop for product-led growth. I come to deepen my craft around in-app guides, onboarding flows, user activation, and product tours—then translate those insights into roadmap bets and experiments my product trios can execute immediately.

Why attend? First, signal over noise: the sessions focus on measurable customer behavior and practical playbooks, not vague inspiration. Second, community: the hallway track and roundtables are some of the best conference networking moments in our field. Third, clarity: I leave with sharper product strategy, a prioritized backlog, and a short list of experiments to validate with customers.

If you’re a first-timer, arrive with intent. Define two or three outcomes you want—such as improving onboarding completion, increasing feature adoption, or tightening product roadmapping and sprint planning—and build your agenda around those goals. Star sessions on product discovery, product strategy, and hands-on Pendo use cases like in-app guides and product tours so your notes translate into immediate action.

Make the most of the community. Treat the hallway track like a scheduled session: set a goal to meet ten peers, bring a crisp introduction, and ask concrete questions such as, “What measurable behavior change did your in-app guide drive?” or “Which activation metric mattered most for your last launch?” Swap templates and dashboards, and follow up within 24 hours while context is fresh.

Logistics matter more than most people admit. Downtown Raleigh is walkable, but high-demand sessions fill quickly—arrive early, wear comfortable shoes, and keep a portable charger handy. Schedule buffer time between talks to debrief, review notes, and have serendipitous conversations with the Pendo team and practitioners who can deepen your approach.

Capture, then operationalize. I use a simple note structure: Insight → Hypothesis → Experiment → Metric. Turn session takeaways into tests (for example, variations of onboarding checklists or empty-state prompts) and define success criteria in advance. Align those experiments with your OKRs and use QBRs to review outcomes, ensuring what you learned at the festival translates into measurable product impact.

Post-event, run an internal readout within a week. Demo two applicable ideas, propose a 30-60-90 day experiment plan, and tie each initiative to a customer behavior metric such as time-to-value, daily active usage, or feature adoption. This is how Pendomonium goes from inspiring to invaluable—by turning insights into shippable, testable work that advances your strategy.

If this is your first Pendomonium, expect high energy, candid conversations, and a wealth of practical tactics you can apply immediately. I’ll be there comparing notes, learning from peers, and sharing what’s worked—and what hasn’t—in scaling product organizations. If you spot me in a session on activation or onboarding, come say hello.

Inspired by this post on Pendo – Best Practices.

January 23, 2026
Why Codeless Product Analytics Wins: Faster Insights, Fewer Bottlenecks, Bigger PLG Results

Every quarter, I watch product teams move from gut feel to data-informed decisions—until instrumentation bottlenecks slow them to a crawl. That’s why I’ve become an advocate for codeless analytics: it removes the dependency on engineering sprints for basic event tracking and lets teams answer product questions in hours, not weeks.

We explain what codeless analytics are, why (and how) Pendo supports them, plus responses to the top three myths about low-code/no-code solutions.

Here’s how I frame it with my teams: codeless analytics enables product managers, designers, and customer success to tag features visually, track interactions, and analyze adoption without shipping code. The goal isn’t to replace engineered events; it’s to accelerate discovery, speed up iteration, and reduce context-switching for developers. In practice, this means cleaner prioritization, faster validation of hypotheses, and tighter product-led growth loops.

Why Pendo? In my experience, Pendo’s codeless model shortens the distance from question to insight. Visual tagging makes event setup accessible, in-app guides and product tours let us experiment with onboarding and activation, and governance controls ensure data remains trustworthy across teams. The result is a unified analytics approach where we reserve custom instrumentation for complex logic while using codeless tracking for everyday product questions.

Let’s address the top three myths I hear most often. Myth 1: “No-code is only for simple use cases.” In reality, most decisions we make weekly—feature adoption, path analysis, funnel drop-offs, and retention analysis—do not require custom code. Codeless analytics handles these well, and when we need deeper context (like server-side events), we complement it with engineered tracking. It’s a both/and, not an either/or.

Myth 2: “Codeless data isn’t accurate.” Accuracy comes from governance, not the method. I set clear standards: naming conventions, tagging reviews, ownership, and periodic audits. With disciplined process, codeless tracking yields consistent, decision-grade data. The added benefit is visibility—non-technical stakeholders can validate the instrumentation themselves, reducing misalignment.

Myth 3: “Engineers must instrument everything to scale.” Engineering time is precious; we should spend it on differentiated capabilities, not on routine click tracking. Codeless analytics scales by empowering product teams to self-serve, while engineering focuses on back-end, performance, and edge cases. When paired with a unified analytics platform and clear data contracts, this model scales cleanly across product lines.

For teams adopting this approach, I recommend a simple operating model: define your core product questions up front, tag features aligned to those questions, connect insights to in-app guides for experiments, and measure user activation and retention continuously. Whether you run Pendo alongside Amplitude analytics or within a broader unified analytics platform, the key is to keep the insight-to-action loop tight.

The future of product analytics is codeless because it puts insights where they belong—directly in the hands of the people designing the experience. When we remove bottlenecks, we learn faster, ship smarter, and drive measurable PLG impact. That’s how we turn product analytics from a reporting function into a competitive advantage.

Inspired by this post on Pendo – Best Practices.

January 22, 2026
I Built a ‘Pendo Wrapped’ in 10 Minutes with Pendo MCP to Boost Adoption and Delight Users

I set out to create a lightweight, high-impact “Pendo Wrapped” experience for our users—and I did it in under 10 minutes with Pendo MCP. As a VP of Product Management, I’m constantly looking for fast, pragmatic ways to turn product insights into moments that drive engagement. This experiment was about transforming raw analytics into a concise, celebratory year‑in‑review that motivates customers to explore more value. When I say “Pendo Wrapped,” I mean a simple, narrative-style summary of usage highlights: what got adopted, which moments mattered, and where value showed up most clearly. Framed well, that story reinforces product‑led growth by reminding users why they chose us, nudging them toward the next best action, and strengthening activation and retention without heavy development work. My approach was straightforward: define a clear objective (celebrate milestones and prompt the next step), choose a focused set of metrics (adoption, engagement, and activation), and target relevant segments. Then I layered the narrative on top of existing analytics using in‑app guides and product tours to deliver the experience where it matters most—inside the product. The reason it took minutes, not hours, is that Pendo MCP let me work with what we already had—segments, saved reports, and proven guide templates—so I could spend time on the story, not the scaffolding. No code, minimal configuration, and a crisp call to action made it feel polished without being heavy. Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement. If you want to replicate this quickly, start by selecting one user segment and three metrics that matter to them, write a two‑sentence narrative that connects those metrics to outcomes, and ship a short in‑app guide with a single, purposeful CTA. That’s enough to deliver a personalized year‑in‑review feel and spark immediate exploration—no new infrastructure required. What surprised me most was how a small, story‑driven touch created outsized alignment across customers and internal teams. It turned analytics into advocacy, reminded our users of the value they’re already getting, and opened the door to deeper adoption. If you’re pursuing product‑led growth, a fast “Pendo Wrapped” is one of the highest‑leverage experiments you can run this week.

Inspired by this post on Pendo – Perspectives.

January 15, 2026
4 Proven Ways to Keep Employees Informed and Engaged—from Onboarding to Lasting Adoption

Keeping employees informed and engaged isn’t just a communications challenge—it’s a product challenge. When we treat internal tools like products with clear activation moments, measurable outcomes, and continuous discovery, adoption moves from hope to habit. Over the years, I’ve seen small changes in how we onboard, communicate, and measure compound into dramatically higher engagement, better compliance, and faster time-to-value.

“How to improve onboarding, compliance, and internal communications within your employee tools.” That question guides my approach end to end—from the moment someone logs in for the first time to the day they become an expert, championing best practices across their team.

First, I personalize onboarding to accelerate user activation. I map the critical first actions and design a lightweight sequence of product tours and in-app guides that surfaces only what matters right now. Progressive disclosure, clear UX writing, and thoughtful tooltip design reduce cognitive load. I measure time-to-first-value, A/B test checklist microcopy to remove friction, and use Intercom or Pendo to deliver contextual walkthroughs by role, location, and permission level. Amplitude analytics helps me validate that the guided path leads to the intended activation event and sustained usage.

Second, I make compliance effortless and measurable. Instead of long trainings, I embed micro-learnings and policy nudges directly in the flow of work, with just-in-time prompts and short, scenario-based confirmations. I segment by role to avoid alert fatigue and localize where regulations require nuance. Completion rates, quiz accuracy, and time-to-complete are tracked alongside qualitative feedback. When compliance messaging underperforms, I run A/B testing on tone, timing, and format, then iterate until adherence is both higher and faster.

Third, I orchestrate internal communications as lifecycle messaging—not announcements. Employees get targeted release notes, role-specific tips, and in-app reminders aligned to their stage: new, adopting, proficient, or champion. I avoid channel sprawl by making the primary source of truth available in the product, then reinforcing it via email or chat only when necessary. CRM integration and audience rules ensure relevance, while a champions network and office hours create human touchpoints that deepen trust and accelerate adoption.

Fourth, I close the loop with analytics and continuous discovery. I instrument key events and run retention analysis to understand which behaviors predict long-term engagement. I look at cohorts before and after a new guide or product tour, and I compare lift in user activation and feature adoption over 14-, 28-, and 90-day windows. Amplitude analytics provides the behavioral picture; surveys, interviews, and passive feedback widgets explain the why. Together, these inputs power a product-led growth approach for internal tools—observable, repeatable, and improvable.

When teams ask where to start, I pilot one persona, one workflow, and one high-value outcome. I define the activation event, instrument it, launch a single targeted in-app guide through Pendo or Intercom, and A/B test the onboarding microcopy. Two weeks later, I review retention cohorts and completion data, talk to users, and either scale the pattern or iterate. That cadence builds credibility quickly because it ties every communication to a measurable result.

The payoff is tangible: faster onboarding, higher compliance, clearer internal communications, and employees who feel supported rather than overwhelmed. With disciplined messaging, smart instrumentation, and ongoing discovery, we can turn internal tools into catalysts for performance—and transform engagement from a campaign into a culture.

Inspired by this post on Pendo – Best Practices.

January 6, 2026
Stop Choosing: Blend Inside-Out and Outside-In Thinking to Accelerate Product-Led Growth

I’ve never seen great products emerge from a one-sided mindset. Inside-out thinking (strategy-first) and outside-in thinking (customer-first) aren’t rivals—they’re a flywheel. When I weave product vision and defensible differentiation together with real customer signals and behavioral data, adoption climbs, engagement deepens, and the roadmap becomes a catalyst for growth rather than a list of features.

For clarity: inside-out anchors on product strategy, value proposition, and the unique capabilities only we can deliver. Outside-in centers on continuous discovery, user research, and telemetry that reveals what customers actually do—not just what they say. At HighLevel, we pair these perspectives in every planning cycle so we’re bold in direction and grounded in evidence.

Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.

That promise captures why the blend matters. Product-led growth lives or dies on moments like activation, time-to-first-value, and day-30 retention. Inside-out thinking ensures we’re building toward a compelling vision; outside-in thinking ensures users can discover, adopt, and realize value through clear onboarding, in-app guides, and contextual product tours.

Here’s how I apply it in practice. We start by articulating the smallest, sharpest version of our strategy—who we serve, the jobs we must win, and the non-negotiable outcomes. Then we pressure-test that thesis with continuous discovery: call snippets, funnel analysis, pathing, and retention analysis by cohort. When friction shows up in onboarding or early feature adoption, we deploy targeted in-app guides and tours to accelerate user activation without bloating the product or training costs.

A simple operating rhythm keeps the balance: begin each quarter with outcomes vs output OKRs tied to adoption and retention; instrument flows to expose drop-offs; ship iterative improvements; and reinforce them with just-in-time guidance. We use outside-in signals to sequence what we tackle next, and inside-out conviction to avoid chasing noise. The result is faster learning cycles and fewer expensive reworks.

Measurement closes the loop. I track activation rate, time-to-first-value, engagement with the few behaviors that predict renewal, and the impact of each guide or tour on completion rates. When we see lift, we codify the pattern; when we don’t, we prune and refocus. That evidence-based cadence keeps teams empowered and stakeholders aligned.

Culture makes this sustainable. Empowered product teams own outcomes, not tickets. Stakeholder management becomes easier when decisions are grounded in a clear strategy and transparent evidence from real users. And customers feel the difference when the product teaches itself—meeting them with the right help, in the right moment, without getting in their way.

If you’ve been choosing between inside-out and outside-in, stop. Fuse them. Lead with a crisp product strategy, listen with humility, and operationalize adoption through purposeful onboarding, in-app guides, and product tours. That’s how we compound learning, reduce risk, cut support costs, and accelerate product-led growth.

Inspired by this post on Pendo – Perspectives.

January 4, 2026